In the medical field, simulators, such as an operation simulator and an organ simulator, are used for determination of a treatment policy, diagnosis, postoperative prediction, development of pharmaceuticals and medical equipment, or the like. In the simulation by such simulators, three-dimensional shape data of organs is used, but it is often not easy to generate the three-dimensional shape data of organs. The reasons are that organs are not visible, and are not directly measurable since they exist in the inside of a living body, and also that the shapes of organs are complicated originally.
Conventionally, there is a technique to generate the three-dimensional shape data used for a simulation by extracting a target region based on brightness values of tomographic images (for example, a Region Growing method) such as CT (Computed Tomography) images and MRI (Magnetic Resonance Imaging) images. For example, assume that a region surrounded by a solid line 21 (a region of the right ventricular fluid) and a region surrounded by a solid line 22 (a region of the left ventricular fluid) in FIG. 2 are to be extracted from tomographic image data of a heart illustrated in FIG. 1 by the Region Growing method, and that the three-dimensional shape data of the right ventricular fluid and the left ventricular fluid are to be generated. However, when the Region Growing method is carried out actually to the tomographic image data of the heart illustrated in FIG. 1, a result illustrated in FIG. 3 is obtained. As illustrated in FIG. 3, a portion of the region of the right ventricular fluid cannot be extracted due to influence of a contrast medium. Moreover, a region corresponding to a papillary muscle in the left ventricular fluid cannot be extracted too. Thus, there is a limit to extract the region based only on the brightness values.
Moreover, there is a technique to find an evaluation function for estimating, based on its feature quantity, whether or not a pixel is a pixel which represents an outline to calculate an evaluation value which represents whether a pixel in the region is a pixel on the outline by using the evaluation function, and to determine the outline based on the evaluation value.
Moreover, there is a technique to minimize crushing of a small edge in an image binarized by an error diffusion method and convert to a clear gray-scale image with high tone. Specifically, at least the edge strength of a target pixel in an inputted binary image is detected, and a threshold of the brightness of the image is set based on the detection result. The size of a multi-level filter is determined by comparing a percentage of black pixels and white pixels in the plural multi-level filters with the threshold. Then, the binary image data is converted to gray-scale image data based on the percentage of the black pixels and white pixels in the multi-level filter with the determined size.
However, it is not possible to generate the three-dimensional shape data with high precision by using techniques as described above because there is a case where a target region can't be extracted accurately.    Patent Document 1: Japanese Laid-open Patent Publication No. 2007-307358    Patent Document 2: Japanese Laid-open Patent Publication No. 08-191387    Non-Patent Document 1: A. Tremeau, N. Borel, “A Region Growing and Merging Algorithm to Color Segmentation”, Pattern recognition, Volume 30, Issue 7, July 1997, Pages 1191-1203